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Genetic Programming Theory and Practice V

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Table of Contents

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    Book Overview
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    Chapter 1 Genetic Programming: Theory and Practice
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    Chapter 2 Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming
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    Chapter 3 Manipulation of Convergence in Evolutionary Systems
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    Chapter 4 Large-Scale, Time-Constrained Symbolic Regression-Classification
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    Chapter 5 Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction
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    Chapter 6 Towards an Information Theoretic Framework for Genetic Programming
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    Chapter 7 Investigating Problem Hardness of Real Life Applications
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    Chapter 8 Improving the Scalability of Generative Representations for Openended Design
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    Chapter 9 Programstructure-Fitnessdisconnect and Its Impact on Evolution in Genetic Programming
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    Chapter 10 Genetic Programmingwith Reuse of Known Designs for Industrially Scalable, Novel Circuit Design
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    Chapter 11 Robust engineering design of electronic circuits with active components using genetic programming and bond Graphs
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    Chapter 12 Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models
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    Chapter 13 Improving Performance and Cooperation in Multi-Agent Systems
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    Chapter 14 An Empirical Study of Multi-Objective Algorithms for Stock Ranking
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    Chapter 15 Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center
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